Wind Bias Research Articles

Impacts of Subgrid Orographic Drag on the Summer Monsoon Circulation and Precipitation in East Asia

AbstractThis work is an investigation of the impacts of three different components of parameterized subgrid orographic drag (SOD) on the summer monsoon circulation and precipitation in East Asia, that is, flow‐blocking drag (FBD), low‐level gravity wave drag (GWD), and upper‐level GWD. Using the global Weather Research and Forecasting model, a series of numerical experiments are conducted with different configurations of SOD parameterization. In the case of no SOD, the simulated lower tropospheric winds are too strong in an elongated region extending from East China to the east of Japan, accompanied with excessive rainfall. The intensity of the western Pacific subtropical high is overestimated, thus suppressing the precipitation over the subtropical ocean. With the parameterization of SOD, the circulation and precipitation biases are alleviated, depending on the geographical and vertical distribution of SOD. The FBD is very weak and only contributes to the circulation in eastern East Asia. Contrastingly, there is remarkable low‐level GWD in the lower latitudes which helps reduce the low‐level wind biases in South China. The upper‐level GWD only occurs in the midlatitudes, preferentially above the subtropical jet. It can not only decelerate the winds in the midlatitudes but also reduce the southwesterlies in East China through the interaction with the upper‐level jet. Alleviation of precipitation bias over the land of East Asia is primarily due to the low‐level GWD which weakens the water vapor flux. In contrast, the upper‐level GWD has the greatest contribution to the enhancement of precipitation over the subtropical ocean by increasing the low‐level lifting.

A New Land-Use Dataset for the Weather Research and Forecasting (WRF) Model

The USGS (United States Geological Survey) land-use data used in the Weather Research and Forecasting (WRF) model have become obsolete as they are unable to accurately represent actual underlying surface features. Therefore, this study developed a new multi-satellite remote-sensing land-use dataset based on the latest GLC2015 (Global Land Cover, 2015) land-use data, which had 300 m spatial resolution. The new data were used to update the default USGS land-use dataset. Based on observational data from national meteorological observing stations in Xinjiang, northwest China, a comparison of the old USGS and new GLC2015 land-use datasets in the WRF model was performed for July 2018, where the simulated variables included the sensible heat flux (SHF), latent heat flux (LHF), surface skin temperature (Tsk), two-meter air temperature (T2), wind speed (Winds), specific humidity (Q2) and relative humidity (RH). The results indicated that there were significant differences between the two datasets. For example, our statistical verification results found via in situ observations made by the MET (model evaluation tools) illustrated that the bias of T2 decreased by 2.54%, the root mean square error (RMSE) decreased by 1.48%, the bias of Winds decreased by 10.46%, and the RMSE decreased by 6.77% when using the new dataset, and the new parameter values performed a net positive effect on land–atmosphere interactions. These results suggested that the GLC2015 land-use dataset developed in this study was useful in terms of improving the performance of the WRF model in the summer months.

Evaluation of the Quasi‐Biennial Oscillation in global climate models for the SPARC QBO‐initiative

AbstractQuasi‐biennial oscillations (QBOs) in thirteen atmospheric general circulation models forced with both observed and annually repeating sea surface temperatures (SSTs) are evaluated. In most models the QBO period is close to, but shorter than, the observed period of 28 months. Amplitudes are within ±20% of the observed QBO amplitude at 10 hPa, but typically about half of that observed at lower altitudes (50 and 70 hPa). For almost all models, the oscillation's amplitude profile shows an overall upward shift compared to reanalysis and its meridional extent is too narrow. Asymmetry in the duration of eastward and westward phases is reasonably well captured, though not all models replicate the observed slowing of the descending westward shear. Westward phases are generally too weak, and most models have an eastward time mean wind bias throughout the depth of the QBO. The intercycle period variability is realistic and in some models is enhanced in the experiment with observed SSTs compared to the experiment with repeated annual cycle SSTs. Mean periods are also sensitive to this difference between SSTs, but only when parametrized non‐orographic gravity wave (NOGW) sources are coupled to tropospheric parameters and not prescribed with a fixed value. Overall, however, modelled QBOs are very similar whether or not the prescribed SSTs vary interannually. A portrait of the overall ensemble performance is provided by a normalized grading of QBO metrics. To simulate a QBO, all but one model used parametrized NOGWs, which provided the majority of the total wave forcing at altitudes above 70 hPa in most models. Hence the representation of NOGWs either explicitly or through parametrization is still a major uncertainty underlying QBO simulation in these present‐day experiments.

Skill Assessment of an Atmosphere–Wave Regional Coupled Model over the East China Sea with a Focus on Typhoons

This study performed several sensitivity experiments to investigate the impact of atmosphere–wave coupling on the simulated wind and waves over the East China Sea (ECS) with a focus on typhoon events. These experiments include stand-alone regional atmosphere model (CCLM) simulations, stand-alone spectral wave model (WAM) simulations driven by the regional atmospheric model CCLM or ERA5 reanalysis, and two-way (CCLM-WAM) coupled simulations. We assessed the simulated wind speed and significant wave height against in situ observations and remote sensing data and focused on typhoon events in 2010. We analyzed the differences between the experiments in capturing the surface pressure, wind speed, and roughness length. Both ERA5 reanalysis data and our regional model simulations demonstrate high quality in capturing wind and wave conditions over the ECS. The results show that downscaled simulations tend to be closer to in situ observations than ERA5 reanalysis data in capturing wind variability and probability distribution, dominant wind and wave directions, strong typhoon intensity and related extreme significant wave height. In comparison with satellite observations, the CCLM-WAM simulation outperforms the CCLM in reducing wind bias. The coupled and uncoupled simulations are very similar in terms of other wind and wave statistics. Though there is much improvement in capturing typhoon intensity to ERA5, regional downscaled simulations still underestimate the wind intensity of tropical cyclones.

The Impacts of Horizontal Resolution on the Seasonally Dependent Biases of the Northeastern Pacific ITCZ in Coupled Climate Models

ABSTRACTThe double-ITCZ bias has puzzled the climate modeling community for more than two decades. Here we show that, over the northeastern Pacific Ocean, precipitation and sea surface temperature (SST) biases are seasonally dependent in the NCAR CESM1 and 37 CMIP5 models, with positive biases during boreal summer–autumn and negative biases during boreal winter–spring, although the easterly wind bias persists year round. This seasonally dependent bias is found to be caused by the model’s failure to reproduce the climatological seasonal wind reversal of the North American monsoon. During winter–spring, the observed easterly wind dominates, so the simulated stronger wind speed enhances surface evaporation and lowers SST. It is opposite when the observed wind turns to westerly during summer–autumn. An easterly wind bias, mainly evident in the lower troposphere, also occurs in the atmospheric model when the observed SST is prescribed, suggesting that it is of atmospheric origin. When the atmospheric model resolution is doubled in the CESM1, both SST and precipitation are improved in association with the reduced easterly wind bias. During boreal spring, when the double-ITCZ bias is most significant, the northern and southern ITCZ can be improved by 29.0% and 18.8%, respectively, by increasing the horizontal resolution in the CESM1. When dividing the 37 CMIP5 models into two groups on the basis of their horizontal resolutions, it is found that both the seasonally dependent biases over the northeastern Pacific and year-round biases over the southeastern Pacific are reduced substantially in the higher-resolution models, with improvement of ~30% in both regions during boreal spring. Close relationships between wind and precipitation biases over the northeastern and southeastern Pacific are also found among CMIP5 models.

Skill and uncertainty in surface wind fields from general circulation models: Intercomparison of bias between AGCM, AOGCM and ESM global simulations

AbstractUnderstanding the reliability of global climate models (GCMs) to reproduce the historical surface wind fields is integral part of building robust projections of surface wind‐climate, and other wind‐dependent geophysical climatic variables. Understanding the skill of atmosphere‐only models (AGCM), coupled atmosphere–ocean models (AOGCM) and fully coupled earth system models (ESM) is likewise paramount to assess any systematic model improvements. In this paper, we systematically assess whether surface wind fields obtained from 28 CMIP5 GCMs can represent large‐scale spatial patterns and temporal variability of historical surface winds. We show that inter‐model uncertainty is typically 2–4 times larger than the uncertainty associated with GCM internal variability, although the latter can be significant within specific regions. We also find that CMIP5 models are typically capable of reliably reproducing large‐scale spatial patterns of historical near‐surface winds, but considerable uncertainty lies within the CMIP5 ensemble with strong latitudinal dependence. CMIP5 models show limitations in their ability to reliably represent inter‐annual and inter‐seasonal variability particularly within tropical‐cyclone‐affected regions. In further analysis, we quantify and intercompare historical wind bias from different types of models with different dynamical cores, based on multiple CMIP5 diagnostic experiments. We find that bias in surface wind fields are largely intrinsic to the atmospheric components of the models, and that the inclusion of carbon‐cycle dynamics has insignificant effect on simulated surface winds (at decadal time‐scales). Inconsistencies between AGCM and AOGCM simulations are largely driven by errors in sea surface temperatures (SST); though such differences are not statistically significant relative to the inter‐model uncertainty within the CMIP5 ensemble. These results show that the dominant source of bias in simulated wind fields lies in the underlying physics of the atmospheric component of the models.

A generalized simulation capability for rotating- beam scatterometers

Abstract. Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam. In our study, a generic simulation frame is established and verified to assess the wind retrieval skill of the three different scatterometers: SCAT on CFOSAT (China France Oceanography SATellite), WindRad (Chinese Wind Radar) on FY-3E, and SeaWinds on QuikSCAT. Besides the comparison of the so-called first rank solution retrieval skill of the input wind field, other figures of merit (FoMs) are applied to statistically characterize the associated wind retrieval performance from three aspects: wind vector root mean square error, ambiguity susceptibility, and wind biases. The evaluation shows that, overall, the wind retrieval quality of the three instruments can be ranked from high to low as WindRad, SCAT, and SeaWinds, where the wind retrieval quality strongly depends on the wind vector cell (WVC) location across the swath. Usually, the higher the number of views, the better the wind retrieval, but the effect of increasing the number of views reaches saturation, considering the fact that the wind retrieval quality at the nadir and sweet swath parts stays relatively similar for SCAT and WindRad. On the other hand, the wind retrieval performance in the outer swath of WindRad is improved substantially as compared to SCAT due to the increased number of views. The results may be generally explained by the different incidence angle ranges of SCAT and WindRad, mainly affecting azimuth diversity around nadir and number of views in the outer swath. This simulation frame can be used for optimizing the Bayesian wind retrieval algorithm, in particular to avoid biases around nadir but also to investigate resolution and accuracy through incorporating and analyzing the spatial response functions of the simulated Level-1B data for each WVC.

Linear effects of nontraditional Coriolis terms on intertropical convergence zone forced large‐scale flow

AbstractThis article promotes a measure to validate the hydrostatic approximation via scaling the nontraditional Coriolis term (NCT) in the zonal momentum equation. To demonstrate the scaling, this study simulates large‐scale flow forced by a prescribed heat source, mimicking the intertropical convergence zone (ITCZ) using a linearized forced‐dissipative model. The model solves two similar equations, between which the only difference is the inclusion of NCTs. The equations are derived using the following approximations: anelastic, equatorial beta‐plane, linearized, zonally symmetric, steady, and a constant dissipation coefficient. The large‐scale flows simulated with and without NCTs are compared in terms of the meridional–vertical circulation, the zonal wind, and the potential temperature. Both results appear like the Hadley circulation. With the model parameters controlled, the differences between the results without NCTs and those with NCTs yield the linear biases due to omitting NCTs. The most prominent bias is a westerly wind bias in the ITCZ heating region that emerges because omitting NCTs prevents the associated westward acceleration when heating‐induced vertical motion is present. The zonal wind bias divided by the zonal wind with NCTs is 0.120 ± 0.007 in terms of the westerly maximum and 0.0452 ± 0.0005 in terms of the root mean square (RMS) when the prescribed ITCZ mimics the observed ITCZ in May over the East Pacific. These normalized measures of the zonal wind bias increase with a narrower ITCZ or an ITCZ closer to the Equator, because of a weaker subtropical jet stream given the same vertical heating profile. This difference can be traced by a nondimensional parameter scaling the ratio of the NCT to the traditional Coriolis term. The scaling encourages the restoration of NCTs into global models.

Skill of dynamical and GHACOF consensus seasonal forecasts of East African rainfall

Seasonal forecasts of rainfall are considered the priority timescale by many users in the tropics. In East Africa, the primary operational seasonal forecast for the region is produced by the Greater Horn of Africa Climate Outlook Forum (GHACOF), and issued ahead of each rainfall season. This study evaluates and compares the GHACOF consensus forecasts with dynamical model forecasts from the UK Met Office GloSea5 seasonal prediction system for the two rainy seasons. GloSea demonstrates positive skill (r = 0.69) for the short rains at 1 month lead. In contrast, skill is low for the long rains due to lack of predictability of driving factors. For both seasons GHACOF forecasts show generally lower levels of skill than GloSea. Several systematic errors within the GHACOF forecasts are identified; the largest being the tendency to over-estimate the likelihood of near normal rainfall, with over 70% (80%) of forecasts giving this category the highest probability in the short (long) rains. In a more detailed evaluation of GloSea, a large wet bias, increasing with forecast lead time, is identified in the short rains. This bias is attributed to a developing cold SST bias in the eastern Indian Ocean, driving an easterly wind bias across the equatorial Indian Ocean. These biases affect the mean state moisture availability, and could act to reduce the ability of the dynamical model in predicting interannual variability, which may also be relevant to predictions from coupled models on longer timescales.

Winter Storm Tracks and Related Weather in the NCEP Climate Forecast System Weeks 3–4 Reforecasts for North America

Abstract This article examines to what extent the NCEP Climate Forecast System (CFS) weeks 3–4 reforecasts reproduce the CFS Reanalysis (CFSR) storm-track properties, and if so, whether the storm-track behavior can contribute to the prediction of related winter weather in North America. The storm tracks are described by objectively tracking isentropic potential vorticity (PV) anomalies for two periods (base, 1983–2002; validation, 2003–10) to assess their value in a more realistic forecast mode. Statistically significant positive PV biases are found in the storm-track reforecasts. Removal of systematic errors is found to improve general storm-track features. CFSR and Reforecast (CFSRR) reproduces well the observed intensity and spatial distributions of storm-track-related near-surface winds, with small yet significant biases found in the storm-track regions. Removal of the mean wind bias further reduces the error on average by 12%. The spatial distributions of the reforecast precipitation correspond well with the reanalysis, although significant positive biases are found across the contiguous United States. Removal of the precipitation bias reduces the error on average by 25%. The bias-corrected fields better depict the observed variability and exhibit additional improvements in the representation of winter weather associated with strong-storm tracks (the storms with more intense PV). Additionally, the reforecasts reproduce the characteristic intensity and frequency of hazardous strong-storm winds. The findings suggest a potential use of storm-track statistics in the advancement of subseasonal-to-seasonal weather prediction in North America.

Improved Surface Layer Simulation Using Refined Vertical Resolution in the GFDL Atmospheric General Circulation Model

AbstractSurface layer (SL) variables (e.g., 2‐m temperature [T2] and 10‐m wind [U10]) are diagnosed by applying the flux‐profile relationships based on Monin‐Obukhov similarity theory to the lowest model height (LMH). This assumes that the LMH is in the SL, which is approximately the bottom 10% of the boundary layer, but atmospheric general circulation models rarely satisfy this in stable boundary layers (SBLs). To assess errors in the diagnostic variables due to the LMH solely linked to the diagnostic algorithm, offline tests of the flux‐profile relationships are performed with LMH from a few meters to 60 m for three SBL regimes: weakly stable, very stable, and transition stability regimes. The results show that T2 and U10 are underestimated by O(0.1–1 °C) and O(0.1–1 m/s), respectively, if the LMH is higher than the SL height. The stronger the SL stability is, the larger the temperature biases are. The negative wind biases increase with the surface stress. Based on these findings, we analyze the impacts of the LMH on the climatologies of the diagnostic parameters in the Geophysical Fluid Dynamics Laboratory (GFDL) Atmosphere Model 4.0/Land Model 4.0. The results show reduced negative biases in T2 and U10 by lowering the LMH. The decrease of the overall bias over land is mainly due to the sensitivity of the diagnostic method to the LMH in SBLs, as shown in the offline tests. The overall increase in T2 and U10 over the oceans results from the increase in the actual near‐surface temperature and wind rather than from the diagnostic method.

Role of wind stress in driving SST biases in the Tropical Atlantic

Coupled climate models used for long-term future climate projections and seasonal or decadal predictions share a systematic and persistent warm sea surface temperature (SST) bias in the tropical Atlantic. This study attempts to better understand the physical mechanisms responsible for the development of systematic biases in the tropical Atlantic using the so-called Transpose-CMIP protocol in a multi-model context. Six global climate models have been used to perform seasonal forecasts starting both in May and February over the period 2000–2009. In all models, the growth of SST biases is rapid. Significant biases are seen in the first month of forecast and, by 6 months, the root-mean-square SST bias is 80% of the climatological bias. These control experiments show that the equatorial warm SST bias is not driven by surface heat flux biases in all models, whereas in the south-eastern Atlantic the solar heat flux could explain the setup of an initial warm bias in the first few days. A set of sensitivity experiments with prescribed wind stress confirm the leading role of wind stress biases in driving the equatorial SST bias, even if the amplitude of the SST bias is model dependent. A reduced SST bias leads to a reduced precipitation bias locally, but there is no robust remote effect on West African Monsoon rainfall. Over the south-eastern part of the basin, local wind biases tend to have an impact on the local SST bias (except in the high resolution model). However, there is also a non-local effect of equatorial wind correction in two models. This can be explained by sub-surface advection of water from the equator, which is colder when the bias in equatorial wind stress is corrected. In terms of variability, it is also shown that improving the mean state in the equatorial Atlantic leads to a beneficial intensification of the Bjerknes feedback loop. In conclusion, we show a robust effect of wind stress biases on tropical mean climate and variability in multiple climate models.

Application of bias corrections to improve hub-height ensemble wind forecasts over the Tehachapi Wind Resource Area

This study demonstrates improvements in ensemble wind forecasts at hub height due to bias correction strategies and their impact on wind energy forecasts at the Alta II wind farm in southern California. The ensemble consists of twenty members that differ in physics schemes used. Ensemble wind forecasts are produced for three months. Hub-height sodar wind observations are used to evaluate forecast performance. Time-dependent bias correction (TBC) and probability bias correction (PBC) are proposed to calibrate hub-height ensemble wind forecasts.The root mean square errors (RMSEs) and biases of forecasted hub-height winds are significantly reduced using both bias correction methods. RMSEs were reduced by 20% and 15% for TBC and PBC, respectively. When evaluating forecast performance from a reliability perspective, PBC better corrects both high and low forecast probabilities for high-wind thresholds. The penalty associated with deviation of the wind energy forecasts from observed values is reduced by 8.7% and 8.0% for TBC and PBC, respectively. It is notable that PBC produces a greater penalty reduction during the high wind bias forecast period (6 p.m.–8 a.m. local standard time); during this time period, PBC reduces the penalty by 25.8% while TBC reduces it by just 19.2%.

The Modeling of the North Equatorial Countercurrent in the Community Earth System Model and its Oceanic Component

AbstractThe North Equatorial Countercurrent (NECC) simulated by a coupled ocean‐atmosphere model and its oceanic component have been investigated and compared against oceanographic observations. Coupled model simulations using the Community Earth System Model version 2 are compared against ocean‐ice simulations forced by the second phase of the Coordinated Ocean‐ice Reference Experiments (CORE) data set. The modeled circulation biases behave differently to the west of and to the east of 120°W: the CORE‐forced ocean model largely underestimates the NECC transport to the west and the coupled model underestimates it to the east. Further analysis suggests that the surface wind stress and its curl is the most important forcing term for correctly simulating the NECC in both models. West of 120°W, the NECC biases in the ocean model are attributed to the southward movement of the maximum easterly trade winds in the Northern Hemisphere and the associated wind stress curl (WSC) pattern; east of 120°W, the NECC biases in the coupled model are attributed to the weak northward cross‐equatorial winds and southwestward gap winds, which lead to a weak WSC gradient at the latitude of NECC. Further analysis confirms that the WSC biases comes mainly from the zonal wind bias, which may in turn relate to the protocol of CORE‐II of adjusting reanalysis winds toward satellite data, which include the relative wind effect.

Technology of Actual Wind Deviation Monitoring for Suspension Insulator Strings Based on Improved Edge Detection

Finding out the wind deviation fault of the insulators of transmission lines in time can not only help people better complete the wind bias warning and prevention work, but also reduce the economic loss and the maintenance difficulty. A technology of Actual Wind Deviation Monitoring for Suspension Insulator Strings Based on Improved Edge Detection is proposed in this paper. It can calculate the actual wind deviation angle and distance of the insulator through image processing technology and camera calibration. Firstly, the video monitoring device is installed on the tower to collect the field insulator images, and the R + G improved grayscale processing and median filtering are carried out. Secondly, two-dimensional Otsu threshold segmentation is applied to the pre-processed image, and the insulator string target is obtained by combining morphological filtering and connection domain extraction. Then, the improved Kirsch operator is used to obtain the complete single-pixel wide edge of the insulator string. Searching the left and right boundary points of each insulator separately and the center points of each insulator can be calculated. Finally, these center points are transformed into world coordinates by camera calibration and fitted by least square method (LSM). The deviation angle $\theta $ of the insulator string can be obtained according to the slope of the fitting line, and the deviation distance $d$ according to the length of the insulator string. When $\theta $ is greater than 7.5° and $d$ is greater than 300mm, an alarm is issued to alert the staff. We analyze the performance of the technology by a series of experiments, the maximum error between the proposed method and the manual method is 8.72% and the minimum is 1.01%. Moreover, by analyzing 200 field insulator images, the identification accuracy are 93.75% and 91.36% respectively, which shows that the proposed method is effective and practical. It provides a new idea for the wind deviation monitoring of suspension insulator strings.

Arctic summer sea-ice seasonal simulation with a coupled model: Evaluation of mean features and biases

Current state of the art weather/climate models are representation of the fully coupled aspects of the components of the earth system. Sea-ice is one of the most important components of these models. Simulation of sea-ice in these models is a challenging problem. In this study, evaluation of the hind-cast data of 14 boreal summer seasons with global coupled model HadGEM3 in its seasonal set-up has been performed over the Arctic region from \(9\mathrm{th}\) May start dates. Along with the biases of the sea-ice variables, related atmosphere and oceanic variables have also been examined. The model evaluation is focused on seasonal mean of sea-ice concentration, sea-ice thickness, ocean surface current, SST, ice-drift velocity and sea-ice extent. To diagnose the sea-ice biases, atmospheric variables like, 10 m wind, 2 m air temperature, sea-level pressure and ocean sub-surface temperatures were also examined. The sea-ice variables were compared with GIOMAS dataset. The atmospheric and the oceanic variables were compared with the ERA Interim and the ECMWF Ocean re-analysis (ORAP5) datasets, respectively. The model could simulate the sea-ice concentration and thickness patterns reasonably well in the Arctic Circle. However, both sea-ice concentration and thickness in the model are underestimated compared to observations. A positive (warm) bias is seen both in 2 m air temperature and SST, which are consistent with the negative sea-ice bias. Biases in ocean current and related ice drift are not related to biases in the atmospheric winds. The magnitude of the oceanic subsurface warm biases is seen to be gradually decreasing with depth, but consistent with sea-ice biases. These analyses indicate a possibility of deeper warm subsurface water in the western Arctic Ocean sector (Pacific and Atlantic exchanges) affecting the negative biases in the sea-ice at the surface. The model is able to simulate reasonably well the summer sea-ice melting process and its inter-annual variability, and has useable skill for application purpose.

An intermediate coupled model for the tropical ocean-atmosphere system

An intermediate ocean-atmosphere coupled model is developed to simulate and predict the tropical interannual variability. Originating from the basic physical framework of the Zebiak-Cane (ZC) model, this tropical intermediate couple model (TICM) extends to the entire global tropics, with a surface heat flux parameterization and a surface wind bias correction added to improve model performance and inter-basin connections. The model well reproduces the variabilities in the tropical Pacific and Indian basins. The simulated El Nino-Southern Oscillation (ENSO) shows a period of 3–4 years and an amplitude of about 2°C, similar to those observed. The variabilities in the Indian Ocean, including the Indian Ocean basin mode (IOBM) and the Indian Ocean Dipole (IOD), are also reasonably captured with a realistic relationship to the Pacific. However, the tropical Atlantic variability in the TICM has a westward bias and is overly influenced by the tropical Pacific. A 47-year hindcast experiment using the TICM for the period of 1970–2016 indicates that ENSO is the most predictable mode in the tropics. Skillful predictions of ENSO can be made one year ahead, similar to the skill of the latest version of the ZC model, while a “spring predictability barrier” still exists as in other models. In the tropical Indian Ocean, the predictability seems much higher in the west than in the east. The correlation skill of IOD prediction reaches 0.5 at a 5-month lead, which is comparable to that of the state-of-the-art coupled general circulation models. The prediction of IOD shows a significant “winter-spring predictability barrier”, implying combined influences from the tropical Pacific and the local sea-air interaction in the eastern Indian Ocean. The TICM has little predictive skill in the equatorial Atlantic for lead times longer than 3 months, which is a common problem of current climate models badly in need of further investigation.

Critical Southern Ocean climate model biases traced to atmospheric model cloud errors

The Southern Ocean is a pivotal component of the global climate system yet it is poorly represented in climate models, with significant biases in upper-ocean temperatures, clouds and winds. Combining Atmospheric and Coupled Model Inter-comparison Project (AMIP5/CMIP5) simulations, with observations and equilibrium heat budget theory, we show that across the CMIP5 ensemble variations in sea surface temperature biases in the 40–60°S Southern Ocean are primarily caused by AMIP5 atmospheric model net surface flux bias variations, linked to cloud-related short-wave errors. Equilibration of the biases involves local coupled sea surface temperature bias feedbacks onto the surface heat flux components. In combination with wind feedbacks, these biases adversely modify upper-ocean thermal structure. Most AMIP5 atmospheric models that exhibit small net heat flux biases appear to achieve this through compensating errors. We demonstrate that targeted developments to cloud-related parameterisations provide a route to better represent the Southern Ocean in climate models and projections.

Melting over the northeast Antarctic Peninsula (1999–2009): evaluation of a high-resolution regional climate model

Abstract. Surface melting over the Antarctic Peninsula (AP) may impact the stability of ice shelves and thus the rate at which grounded ice is discharged into the ocean. Energy and mass balance models are needed to understand how climatic change and atmospheric circulation variability drive current and future melting. In this study, we evaluate the regional climate model MAR over the AP at a 10 km spatial resolution between 1999 and 2009, a period when active microwave data from the QuikSCAT mission is available. This model has been validated extensively over Greenland, has is applied here to the AP at a high resolution and for a relatively long time period (full outputs are available to 2014). We find that melting in the northeastern AP, the focus area of this study, can be initiated both by sporadic westerly föhn flow over the AP mountains and by northerly winds advecting warm air from lower latitudes. A comparison of MAR with satellite and automatic weather station (AWS) data reveals that satellite estimates show greater melt frequency, a larger melt extent, and a quicker expansion to peak melt extent than MAR in the centre and east of the Larsen C ice shelf. These differences are reduced in the north and west of the ice shelf, where the comparison with satellite data suggests that MAR is accurately capturing melt produced by warm westerly winds. MAR shows an overall warm bias and a cool bias at temperatures above 0 ∘C as well as fewer warm, strong westerly winds than reported by AWS stations located on the eastern edge of the Larsen C ice shelf, suggesting that the underestimation of melt in this region may be the product of limited eastward flow. At higher resolutions (5 km), MAR shows a further increase in wind biases and a decrease in meltwater production. We conclude that non-hydrostatic models at spatial resolutions better than 5 km are needed to better-resolve the effects of föhn winds on the eastern edges of the Larsen C ice shelf.

Evaluation of Satellite Surface Winds in Relation to Weather Regimes Over the Indian Ocean Using DYNAMO Observations

AbstractSatellites provide the most extensive surface wind data over the tropical oceans, but sampling limitations, rain contamination, and algorithm uncertainties remain. In this study, scatterometer and passive microwave swath data, and multisatellite products are evaluated using Dynamics of the Madden‐Julian Oscillation (DYNAMO) ship and moored buoy wind speed data over the Indian Ocean from September 2011 to January 2012. Each of the satellite products has <2 m/s error, consistent with previous studies. Blended products have error and bias characteristics comparable with swath data and provide better coverage. The errors and biases depend on the prevailing weather regime. The equatorial region is characterized by weeks of winds <5 m/s with little rain punctuated by episodes of winds >10 m/s and heavy rainfall associated with MJO events. The blended products resolve the episodic enhanced equatorial winds associated with the active phase of the MJO. However, due to limited sampling and rain contamination, the swath data did not fully resolve the peak MJO winds. In the Southern Hemisphere trade wind regime, rain contamination is less of a factor and winds are steadier. Satellite wind estimates of equatorial (trade wind) regime tended to be biased low (high). The surface wind products underestimate higher winds during the active MJO due to limited sampling and have high wind bias in the trade wind regime. These weather regime‐dependent biases should be taken into account for applications using these wind products for ocean circulation studies and estimating air‐sea fluxes over the Indian Ocean.